Introduction

More Alaska voters than ever are voting by mail or in early voting this year. This page tracks the numbers as reported by the state.

The data come from the Alaska Division of Elections website It’s a 10-page pdf, so I ran a script using tabula-py to extract the data. Additional summary information is available here.I have republished the data here, where you can download the reports from each day. I had a google sheet that was updating automatically, but that kept breaking. Other caveats: this involves getting reports from all across the state, so there are probably reporting delays.

If you see any errors, contact Ben Matheson. Disclaimer - this may not be fully accurate or up to date. It also may break at any time. This is not official or affiliated with anything…enjoy!

Statewide Ballots Sent, Received, and Rejected

Mail Ballots Sent Mail Ballots Received Percent Received Mail Ballots Rejected
115,453 43,745 37.89% 156

Note: these numbers should match what the state has published here.

Vote By Mail per Alaska House District

Early Voting

Early voting started Monday, October 19th. This will have that data soon.

Mail Ballots Sent to Voters, Received by State, and Rejected

District Number District Ballots Received Ballots Sent Mail Ballots Rejected
28 South Anchorage 2030 6007 4
33 Downtown Juneau/Douglas/Haines/ Skagway 1810 4942 9
14 Eagle River/Chugach State Park 1751 4271 10
24 Anchorage - Oceanview 1568 4439 4
31 Homer/South Kenai 1554 4073 9
27 Anchorage - Basher 1472 3977 2
21 West Anchorage 1467 4215 3
20 Anchorage - Downtown 1466 3648 3
04 Western Fairbanks 1423 3425 3
18 Anchorage - Spenard 1387 3591 12
25 Anchorage - Abbott 1380 3681 4
26 Anchorage - Huffman 1378 4152 3
34 Mendenhall Valley 1373 3918 7
22 Anchorage - Sand Lake 1360 3897 0
35 Sitka/Petersburg 1308 3196 9
16 Anchorage - College Gate 1266 3285 2
29 North Kenai 1219 2952 3
17 Anchorage - University 1169 3015 1
06 Eielson/Denali/Upper Yukon/Border Region 1117 2359 1
11 Greater Palmer 1088 2603 7
12 Chugiak/Gateway 1083 2912 2
23 Anchorage - Taku 1060 2903 6
05 Chena Ridge/Airport 1045 2651 5
10 Rural Mat-Su 1045 2687 0
30 Kenai/Soldotna 979 2697 5
36 Ketchikan/Wrangell/Metlakatla/Hydaburg 979 2139 5
13 Fort Richardson/North Eagle River 975 2520 3
09 Richardson Hwy/East Mat-Su 933 2487 4
01 Downtown Fairbanks 904 2074 2
08 Big Lake/Point Mackenzie 824 2144 5
19 Anchorage - Mountainview 788 2077 3
07 Greater Wasilla 785 2175 4
15 Elmendorf 758 2128 5
32 Kodiak/Cordova/Seldovia 746 2299 2
03 North Pole/Badger 654 1633 1
02 Fairbanks/Wainwright 622 1457 1
37 Bristol Bay/Aleutians/Upper Kuskokwim 393 1121 4
38 Lower Kuskokwim 233 621 0
39 Bering Straits/Yukon Delta 176 509 2
40 Arctic 148 520 1
99 NA 29 53 0

Time Series of Mail Ballots

I have data begining October 14th. This should show how ballots come in as more ballots come in each day.

Mail Voting Relative to Voter Registration and Voter Turnout per House District

For each Alaska House district, I have the number of registered voters (as of October 3, this report). You can see which districts are seeing more take-up of mail voting relative to their voter base. Additional this compares the 2020 completed mail ballots to the full 2016 election turnout.

This is not really finished yet. Also I just made up the regional labels.

Partisan Effects on Vote by Mail

This looks at the relationship between relative vote-by-mail activity and voting results from the 2016 presidential election. The y axis is the percentage of mail votes returned relative to the total 2016 turnout. The x axis and color is the margin by which Donald Trump won or lost the district in 2016. The basic trend you see is that the redder the disrict, the less vote-by-mail there is, so far. The key exception is western Alaska (in the lower left), which voted for Clinton but is not seeing much vote-by-mail participation yet.

Voting Method Breakdown by House District

About

The Alaska Division of Elections data is originally is published in a 10-page PDF that I parsed to extract the data. This uses a combination of R and Python. The Python uses Tabula to pull out the data. After that, an R script cleans out extra spaces, gaps, and labels the rows by house district and adds descriptions. I wanted to do everything in R, but I couldn’t get rJava loaded for the Tabulizer, so the tabula-py library ended up being more expedient.

This page is an RMarkdown document that calculates some summary stats, like percent rejected and then displays the data in several ggplot2 plots. The PDF parsing in particular may be brittle and this could definitely break at anytime.